Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure

Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu

Keywords: Cosmological Parameter Estimation, Persistent Homology, Convolutional Neural Networks, Large-scale Structure

TL;DR: We can use machine learning to infer cosmological information from the topology of the large-scale structure of the universe.

Abstract:

The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from this tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model makes accurate and precise estimates, considerably outperforming Bayesian inference approaches.

Submission Number: 77